@techreport{dowlin2016cryptonets,
author = {Dowlin, Nathan and Gilad-Bachrach, Ran and Laine, Kim and Lauter, Kristin and Naehrig, Michael and Wernsing, John},
title = {CryptoNets: Applying Neural Networks to Encrypted Data with High Throughput and Accuracy},
year = {2016},
month = {February},
publisher = {Microsoft Research},
url = {https://www.microsoft.com/en-us/research/publication/cryptonets-applying-neural-networks-to-encrypted-data-with-high-throughput-and-accuracy/},
number = {MSR-TR-2016-3},
}

@article{Fan2012SomewhatPF,
  title={Somewhat Practical Fully Homomorphic Encryption},
  author={Junfeng Fan and Frederik Vercauteren},
  journal={IACR Cryptology ePrint Archive},
  year={2012},
  volume={2012},
  pages={144}
}

@article{firstHomEnc,
  title={On data banks and privacy homomorphisms},
  author={Ronald L. Rivest, Len Adleman and Michael L. Dertouzos},
  year={1978}
}

@InProceedings{10.1007/978-3-642-29011-4_28,
author="Gentry, Craig
and Halevi, Shai
and Smart, Nigel P.",
editor="Pointcheval, David
and Johansson, Thomas",
title="Fully Homomorphic Encryption with Polylog Overhead",
booktitle="Advances in Cryptology -- EUROCRYPT 2012",
year="2012",
publisher="Springer Berlin Heidelberg",
address="Berlin, Heidelberg",
pages="465--482",
abstract="We show that homomorphic evaluation of (wide enough) arithmetic circuits can be accomplished with only polylogarithmic overhead. Namely, we present a construction of fully homomorphic encryption (FHE) schemes that for security parameter $\lambda$ can evaluate any width-$\Omega$($\lambda$) circuit with t gates in time {\$}t{\backslash}cdot {\backslash}mbox{\{}polylog{\}}({\backslash}lambda ){\$}.",
isbn="978-3-642-29011-4"
}

@article{seal-manual,
  author = {Chen, Hao and Han, Kyoohyung and Huang, Zhicong and Jalali, Amir and Laine, Kim},
  title = {Simple Encrypted Arithmetic Library v2.3.0},
  year = {2018},
  publisher = {Microsoft}
}

@misc{open-letter,
  title = {An Open Letter: research priorities for robust and beneficial artificial intelligence},
  howpublished = {https://futureoflife.org/ai-open-letter}
}

@misc{safeai,
  title = {Building Safe A.I.: A Tutorial for Encrypted Deep Learning},
  year = {2017},
  howpublished = {https://iamtrask.github.io/2017/03/17/safe-ai/}
}

@misc{numerai,
  author = {Richard Craib},
  title = {Encrypted Data For Efficient Markets},
  year = {2016},
  howpublished = {https://medium.com/numerai/encrypted-data-for-efficient-markets-fffbe9743ba8}
}

